Existing technologies for quantifying the capabilities of a particular system include static and dynamic methods. Static methods determine the capacity by static metrics usually hardware stats such as the central processing unit (CPU) speed, the CPU MIPS (millions of instructions per second) metrics, and the amount of memory available. A major problem with these static methods is that the processing capacity of a system can change depending on what the system administrator intends to run on it. A dynamic approach may be better at providing accurate capacity estimates in real-time environments. Secondly, different applications use system resources differently. This can mean that there is not a direct relationship between CPU speed and the capacity of a particular machine versus another to do a particular type of work.
Dynamic methods assume the capacity may change and determine the current capacity of the system taking into consideration that current applications or operating system processes may affect this capacity. Some dynamic methods are set forth below:
1) Remaining CPU utilization: In this method, a maximum CPU utilization is assumed (usually 100%). The difference between the maximum and the current CPU utilization would be considered the remaining capacity. If multiple CPUs exist on a system, this remaining capacity available on all CPUs would then be summed together to form the capacity of the system. There are however several problems with this approach: (a) CPUs and the operating systems that use them are different and do not guarantee that the remaining CPU will give the administrator an accurate or consistent view of the remaining capacity. (b) The bottleneck limiting the capacity of a system may not be the CPU.
2) Protocol handshake measurements: This method is used to estimate the capacity of network applications to handle more work by measuring the speed in which the network layer of the system can establish a network connection to a port the application is listening on. The problem with this approach is that it only evaluates the network and the network stack of the system. It has little or no bearing on the system's ability or capacity to process the work once it arrives.
The embodiment of the invention discuss herein provides several benefits over current techniques. Capacity estimates can be different depending on the platform and processes currently running on that platform. In addition, the behavior of the application itself may require resources in a manner which is difficult for static hardware resources to correlate to. This method would therefore be more accurate than the static methods listed above. The method also has several advantages over the listed dynamic methods for system capacity estimation. The remaining CPU utilization method makes assumptions that every system can achieve the same maximum utilization. It also assumes that the bottleneck of system capacity is the CPU.
Lastly, this method also assumes that the performance of the system or application at high utilization values is acceptable to the administrator. There is a need for a method that solves these problems by learning the maximum capacity levels through the completion of administrator-defined successful transactions. Many applications and multi-hop system strategies could benefit from understanding the amount of work a particular system or application is capable of performing. A metric such as this can be very difficult to calculate due to widely varying system hardware, operating system architectures, application behavior/performance, and so forth.